693 lines
23 KiB
PHP
693 lines
23 KiB
PHP
<?php
|
|
class DocumentProcessor {
|
|
private $srcImage;
|
|
private $processedImage;
|
|
private $outputPath;
|
|
private $imageQualityMetrics;
|
|
|
|
public function __construct() {
|
|
if (!extension_loaded('gd')) {
|
|
throw new Exception('GD extension is required');
|
|
}
|
|
}
|
|
|
|
public function processDocument($imagePath, $outputPath) {
|
|
$this->outputPath = $outputPath;
|
|
|
|
// Detecta o tipo da imagem e carrega
|
|
$imageInfo = getimagesize($imagePath);
|
|
if ($imageInfo === false) {
|
|
throw new Exception('Failed to load image');
|
|
}
|
|
|
|
switch ($imageInfo[2]) {
|
|
case IMAGETYPE_JPEG:
|
|
$this->srcImage = imagecreatefromjpeg($imagePath);
|
|
break;
|
|
case IMAGETYPE_PNG:
|
|
$this->srcImage = imagecreatefrompng($imagePath);
|
|
break;
|
|
default:
|
|
throw new Exception('Unsupported image type');
|
|
}
|
|
|
|
if ($this->srcImage === false) {
|
|
throw new Exception('Failed to create image resource');
|
|
}
|
|
|
|
// Analisa qualidade da imagem e define parâmetros
|
|
$this->analyzeImageQuality()
|
|
->determineOptimalParameters();
|
|
|
|
// Pipeline de processamento adaptativo
|
|
$this->detectAndCropDocument()
|
|
->convertToGrayscale()
|
|
->applyAdaptiveEnhancements()
|
|
->saveResult();
|
|
|
|
return $this->outputPath;
|
|
}
|
|
|
|
private function detectAndCropDocument() {
|
|
$width = imagesx($this->srcImage);
|
|
$height = imagesy($this->srcImage);
|
|
|
|
// Cria cópia para processamento
|
|
$work = imagecreatetruecolor($width, $height);
|
|
imagecopy($work, $this->srcImage, 0, 0, 0, 0, $width, $height);
|
|
|
|
// Pré-processamento para melhorar detecção
|
|
imagefilter($work, IMG_FILTER_GAUSSIAN_BLUR);
|
|
imagefilter($work, IMG_FILTER_GRAYSCALE);
|
|
|
|
// Detecta bordas usando Sobel
|
|
$edges = $this->detectEdgesSobel($work);
|
|
|
|
// Encontra pontos de contorno
|
|
$contourPoints = [];
|
|
for ($x = 0; $x < $width; $x++) {
|
|
for ($y = 0; $y < $height; $y++) {
|
|
$rgb = imagecolorat($edges, $x, $y);
|
|
if ($rgb > 0) {
|
|
$contourPoints[] = [$x, $y];
|
|
}
|
|
}
|
|
}
|
|
|
|
// Encontra os 4 cantos do documento
|
|
$corners = $this->findCornerPoints($contourPoints);
|
|
|
|
if ($corners) {
|
|
// Aplica transformação perspectiva
|
|
$this->processedImage = $this->cropAndTransform($this->srcImage, $corners);
|
|
} else {
|
|
$this->processedImage = $this->srcImage;
|
|
}
|
|
|
|
imagedestroy($work);
|
|
imagedestroy($edges);
|
|
|
|
return $this;
|
|
}
|
|
|
|
private function detectEdgesSobel($image) {
|
|
$width = imagesx($image);
|
|
$height = imagesy($image);
|
|
$edges = imagecreatetruecolor($width, $height);
|
|
|
|
// Kernels Sobel
|
|
$sobelX = [[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]];
|
|
$sobelY = [[-1, -2, -1], [0, 0, 0], [1, 2, 1]];
|
|
|
|
for ($x = 1; $x < $width-1; $x++) {
|
|
for ($y = 1; $y < $height-1; $y++) {
|
|
$pixelX = $pixelY = 0;
|
|
|
|
// Aplica kernels
|
|
for ($i = -1; $i <= 1; $i++) {
|
|
for ($j = -1; $j <= 1; $j++) {
|
|
$rgb = imagecolorat($image, $x + $i, $y + $j);
|
|
$colors = imagecolorsforindex($image, $rgb);
|
|
$gray = $colors['red'];
|
|
|
|
$pixelX += $gray * $sobelX[$i+1][$j+1];
|
|
$pixelY += $gray * $sobelY[$i+1][$j+1];
|
|
}
|
|
}
|
|
|
|
// Magnitude do gradiente
|
|
$magnitude = min(255, sqrt($pixelX * $pixelX + $pixelY * $pixelY));
|
|
|
|
// Aplica threshold para reduzir ruído
|
|
$magnitude = ($magnitude > 50) ? 255 : 0;
|
|
|
|
$color = imagecolorallocate($edges, $magnitude, $magnitude, $magnitude);
|
|
imagesetpixel($edges, $x, $y, $color);
|
|
}
|
|
}
|
|
|
|
return $edges;
|
|
}
|
|
|
|
private function findDocumentBounds($binary) {
|
|
$width = imagesx($binary);
|
|
$height = imagesy($binary);
|
|
$left = $width;
|
|
$right = 0;
|
|
$top = $height;
|
|
$bottom = 0;
|
|
|
|
// Encontra os limites do documento
|
|
for ($x = 0; $x < $width; $x++) {
|
|
for ($y = 0; $y < $height; $y++) {
|
|
$rgb = imagecolorat($binary, $x, $y);
|
|
if ($rgb == 0) { // Pixel preto
|
|
$left = min($left, $x);
|
|
$right = max($right, $x);
|
|
$top = min($top, $y);
|
|
$bottom = max($bottom, $y);
|
|
}
|
|
}
|
|
}
|
|
|
|
if ($left < $right && $top < $bottom) {
|
|
return [
|
|
'left' => $left,
|
|
'right' => $right,
|
|
'top' => $top,
|
|
'bottom' => $bottom
|
|
];
|
|
}
|
|
|
|
return null;
|
|
}
|
|
|
|
private function isEdgePoint($img, $x, $y) {
|
|
$threshold = 30;
|
|
|
|
// Verifica pixels vizinhos
|
|
$center = $this->getPixelIntensity($img, $x, $y);
|
|
$left = $this->getPixelIntensity($img, $x - 1, $y);
|
|
$right = $this->getPixelIntensity($img, $x + 1, $y);
|
|
$top = $this->getPixelIntensity($img, $x, $y - 1);
|
|
$bottom = $this->getPixelIntensity($img, $x, $y + 1);
|
|
|
|
return (abs($center - $left) > $threshold ||
|
|
abs($center - $right) > $threshold ||
|
|
abs($center - $top) > $threshold ||
|
|
abs($center - $bottom) > $threshold);
|
|
}
|
|
|
|
private function getPixelIntensity($img, $x, $y) {
|
|
if ($x < 0 || $y < 0 || $x >= imagesx($img) || $y >= imagesy($img)) {
|
|
return 0;
|
|
}
|
|
|
|
$rgb = imagecolorat($img, $x, $y);
|
|
$colors = imagecolorsforindex($img, $rgb);
|
|
return ($colors['red'] + $colors['green'] + $colors['blue']) / 3;
|
|
}
|
|
|
|
private function findCornerPoints($points) {
|
|
// Encontra os pontos mais extremos
|
|
$topLeft = null;
|
|
$topRight = null;
|
|
$bottomLeft = null;
|
|
$bottomRight = null;
|
|
$minSum = PHP_FLOAT_MAX;
|
|
$maxSum = PHP_FLOAT_MIN;
|
|
$minDiff = PHP_FLOAT_MAX;
|
|
$maxDiff = PHP_FLOAT_MIN;
|
|
|
|
foreach ($points as $point) {
|
|
$sum = $point[0] + $point[1];
|
|
$diff = $point[0] - $point[1];
|
|
|
|
if ($sum < $minSum) {
|
|
$minSum = $sum;
|
|
$topLeft = $point;
|
|
}
|
|
if ($sum > $maxSum) {
|
|
$maxSum = $sum;
|
|
$bottomRight = $point;
|
|
}
|
|
if ($diff < $minDiff) {
|
|
$minDiff = $diff;
|
|
$bottomLeft = $point;
|
|
}
|
|
if ($diff > $maxDiff) {
|
|
$maxDiff = $diff;
|
|
$topRight = $point;
|
|
}
|
|
}
|
|
|
|
if ($topLeft && $topRight && $bottomLeft && $bottomRight) {
|
|
return [$topLeft, $topRight, $bottomRight, $bottomLeft];
|
|
}
|
|
|
|
return null;
|
|
}
|
|
|
|
private function cropAndTransform($img, $corners) {
|
|
$width = abs($corners[1][0] - $corners[0][0]);
|
|
$height = abs($corners[3][1] - $corners[0][1]);
|
|
|
|
$dest = imagecreatetruecolor($width, $height);
|
|
|
|
// Preserva transparência
|
|
imagealphablending($dest, false);
|
|
imagesavealpha($dest, true);
|
|
|
|
// Define pontos de destino
|
|
$dest_points = [
|
|
0, 0,
|
|
$width - 1, 0,
|
|
$width - 1, $height - 1,
|
|
0, $height - 1
|
|
];
|
|
|
|
// Define pontos de origem
|
|
$src_points = [];
|
|
foreach ($corners as $corner) {
|
|
$src_points[] = $corner[0];
|
|
$src_points[] = $corner[1];
|
|
}
|
|
|
|
// Aplica transformação perspectiva
|
|
imagecopyresampled(
|
|
$dest, $img,
|
|
0, 0, min($src_points[0], $src_points[6]), min($src_points[1], $src_points[3]),
|
|
$width, $height,
|
|
max($src_points[2], $src_points[4]) - min($src_points[0], $src_points[6]),
|
|
max($src_points[5], $src_points[7]) - min($src_points[1], $src_points[3])
|
|
);
|
|
|
|
return $dest;
|
|
}
|
|
|
|
private function convertToGrayscale() {
|
|
if (!isset($this->processedImage)) {
|
|
$this->processedImage = $this->srcImage;
|
|
}
|
|
|
|
imagefilter($this->processedImage, IMG_FILTER_GRAYSCALE);
|
|
return $this;
|
|
}
|
|
|
|
private function enhanceContrast() {
|
|
$width = imagesx($this->processedImage);
|
|
$height = imagesy($this->processedImage);
|
|
|
|
// Calcula histograma
|
|
$histogram = array_fill(0, 256, 0);
|
|
for ($x = 0; $x < $width; $x++) {
|
|
for ($y = 0; $y < $height; $y++) {
|
|
$rgb = imagecolorat($this->processedImage, $x, $y);
|
|
$colors = imagecolorsforindex($this->processedImage, $rgb);
|
|
$histogram[$colors['red']]++;
|
|
}
|
|
}
|
|
|
|
// Encontra limites para stretch
|
|
$min = 0;
|
|
$max = 255;
|
|
$pixelCount = $width * $height;
|
|
$cutoffPercent = 0.005; // 0.5%
|
|
|
|
$sum = 0;
|
|
for ($i = 0; $i < 256; $i++) {
|
|
$sum += $histogram[$i];
|
|
if ($sum >= $pixelCount * $cutoffPercent) {
|
|
$min = $i;
|
|
break;
|
|
}
|
|
}
|
|
|
|
$sum = 0;
|
|
for ($i = 255; $i >= 0; $i--) {
|
|
$sum += $histogram[$i];
|
|
if ($sum >= $pixelCount * $cutoffPercent) {
|
|
$max = $i;
|
|
break;
|
|
}
|
|
}
|
|
|
|
// Aplica contrast stretching
|
|
for ($x = 0; $x < $width; $x++) {
|
|
for ($y = 0; $y < $height; $y++) {
|
|
$rgb = imagecolorat($this->processedImage, $x, $y);
|
|
$colors = imagecolorsforindex($this->processedImage, $rgb);
|
|
$gray = $colors['red'];
|
|
|
|
// Normaliza valor
|
|
$newGray = ($gray - $min) * (255 / ($max - $min));
|
|
$newGray = min(255, max(0, $newGray));
|
|
|
|
$color = imagecolorallocate($this->processedImage, $newGray, $newGray, $newGray);
|
|
imagesetpixel($this->processedImage, $x, $y, $color);
|
|
}
|
|
}
|
|
|
|
return $this;
|
|
}
|
|
|
|
private function sharpenImage() {
|
|
$sharpenMatrix = [
|
|
[-1, -1, -1],
|
|
[-1, 16, -1],
|
|
[-1, -1, -1]
|
|
];
|
|
|
|
$this->applyConvolution($sharpenMatrix);
|
|
|
|
// Ajuste fino pós-sharpening
|
|
imagefilter($this->processedImage, IMG_FILTER_CONTRAST, -5);
|
|
|
|
return $this;
|
|
}
|
|
|
|
private function applyConvolution($matrix) {
|
|
$width = imagesx($this->processedImage);
|
|
$height = imagesy($this->processedImage);
|
|
$temp = imagecreatetruecolor($width, $height);
|
|
imagecopy($temp, $this->processedImage, 0, 0, 0, 0, $width, $height);
|
|
|
|
$matrixSize = count($matrix);
|
|
$halfSize = floor($matrixSize / 2);
|
|
|
|
for ($x = $halfSize; $x < $width - $halfSize; $x++) {
|
|
for ($y = $halfSize; $y < $height - $halfSize; $y++) {
|
|
$r = $g = $b = 0;
|
|
|
|
for ($i = -$halfSize; $i <= $halfSize; $i++) {
|
|
for ($j = -$halfSize; $j <= $halfSize; $j++) {
|
|
$rgb = imagecolorat($temp, $x + $i, $y + $j);
|
|
$colors = imagecolorsforindex($temp, $rgb);
|
|
|
|
$weight = $matrix[$i + $halfSize][$j + $halfSize];
|
|
$r += $colors['red'] * $weight;
|
|
$g += $colors['green'] * $weight;
|
|
$b += $colors['blue'] * $weight;
|
|
}
|
|
}
|
|
|
|
$r = min(255, max(0, $r));
|
|
$g = min(255, max(0, $g));
|
|
$b = min(255, max(0, $b));
|
|
|
|
$color = imagecolorallocate($this->processedImage, $r, $g, $b);
|
|
imagesetpixel($this->processedImage, $x, $y, $color);
|
|
}
|
|
}
|
|
|
|
imagedestroy($temp);
|
|
return $this;
|
|
}
|
|
|
|
private function removeNoise() {
|
|
// Aplica filtro mediano
|
|
$temp = imagecreatetruecolor(imagesx($this->processedImage), imagesy($this->processedImage));
|
|
imagecopy($temp, $this->processedImage, 0, 0, 0, 0, imagesx($this->processedImage), imagesy($this->processedImage));
|
|
|
|
for ($x = 1; $x < imagesx($this->processedImage) - 1; $x++) {
|
|
for ($y = 1; $y < imagesy($this->processedImage) - 1; $y++) {
|
|
$values = array();
|
|
// Coleta valores dos pixels vizinhos
|
|
for ($i = -1; $i <= 1; $i++) {
|
|
for ($j = -1; $j <= 1; $j++) {
|
|
$rgb = imagecolorat($temp, $x + $i, $y + $j);
|
|
$colors = imagecolorsforindex($temp, $rgb);
|
|
$values[] = $colors['red'];
|
|
}
|
|
}
|
|
sort($values);
|
|
$median = $values[4]; // Valor mediano
|
|
$color = imagecolorallocate($this->processedImage, $median, $median, $median);
|
|
imagesetpixel($this->processedImage, $x, $y, $color);
|
|
}
|
|
}
|
|
|
|
imagedestroy($temp);
|
|
return $this;
|
|
}
|
|
|
|
private function adjustBrightnessContrast() {
|
|
// Ajustes mais suaves
|
|
imagefilter($this->processedImage, IMG_FILTER_BRIGHTNESS, 5);
|
|
imagefilter($this->processedImage, IMG_FILTER_CONTRAST, -8);
|
|
return $this;
|
|
}
|
|
|
|
private function normalizeBackground() {
|
|
$width = imagesx($this->processedImage);
|
|
$height = imagesy($this->processedImage);
|
|
|
|
// Calcula média e desvio padrão do fundo
|
|
$samples = [];
|
|
$borderSize = 10;
|
|
|
|
// Amostra pixels das bordas
|
|
for ($x = 0; $x < $width; $x++) {
|
|
for ($y = 0; $y < $borderSize; $y++) {
|
|
$rgb = imagecolorat($this->processedImage, $x, $y);
|
|
$colors = imagecolorsforindex($this->processedImage, $rgb);
|
|
$samples[] = $colors['red'];
|
|
}
|
|
for ($y = $height - $borderSize; $y < $height; $rgb++) {
|
|
$colors = imagecolorsforindex($this->processedImage, $rgb);
|
|
$samples[] = $colors['red'];
|
|
}
|
|
}
|
|
|
|
$mean = array_sum($samples) / count($samples);
|
|
$stdDev = sqrt(array_sum(array_map(function($x) use ($mean) {
|
|
return pow($x - $mean, 2);
|
|
}, $samples)) / count($samples));
|
|
|
|
// Normaliza fundo usando estatísticas
|
|
$threshold = $mean - ($stdDev * 2.5);
|
|
for ($x = 0; $x < $width; $x++) {
|
|
for ($y = 0; $y < $height; $y++) {
|
|
$rgb = imagecolorat($this->processedImage, $x, $y);
|
|
$colors = imagecolorsforindex($this->processedImage, $rgb);
|
|
|
|
if ($colors['red'] > $threshold) {
|
|
$color = imagecolorallocate($this->processedImage, 255, 255, 255);
|
|
imagesetpixel($this->processedImage, $x, $y, $color);
|
|
}
|
|
}
|
|
}
|
|
|
|
return $this;
|
|
}
|
|
|
|
private function saveResult() {
|
|
if (!isset($this->processedImage)) {
|
|
throw new Exception('No processed image to save');
|
|
}
|
|
|
|
$extension = strtolower(pathinfo($this->outputPath, PATHINFO_EXTENSION));
|
|
|
|
switch ($extension) {
|
|
case 'jpg':
|
|
case 'jpeg':
|
|
imagejpeg($this->processedImage, $this->outputPath, 90);
|
|
break;
|
|
case 'png':
|
|
imagepng($this->processedImage, $this->outputPath, 9);
|
|
break;
|
|
default:
|
|
throw new Exception('Unsupported output image type');
|
|
}
|
|
|
|
if (is_resource($this->srcImage)) {
|
|
imagedestroy($this->srcImage);
|
|
}
|
|
if (is_resource($this->processedImage)) {
|
|
imagedestroy($this->processedImage);
|
|
}
|
|
|
|
return $this;
|
|
}
|
|
|
|
private function getAdaptiveThreshold($image) {
|
|
$width = imagesx($image);
|
|
$height = imagesy($image);
|
|
$blockSize = 40; // Tamanho do bloco para threshold adaptativo
|
|
$C = 10; // Constante de ajuste
|
|
|
|
// Cria imagem para o resultado
|
|
$result = imagecreatetruecolor($width, $height);
|
|
|
|
// Para cada bloco
|
|
for ($x = 0; $x < $width; $x += $blockSize) {
|
|
for ($y = 0; $y < $height; $y += $blockSize) {
|
|
// Calcula média do bloco
|
|
$sum = 0;
|
|
$count = 0;
|
|
|
|
$blockW = min($blockSize, $width - $x);
|
|
$blockH = min($blockSize, $height - $y);
|
|
|
|
for ($bx = 0; $bx < $blockW; $bx++) {
|
|
for ($by = 0; $by < $blockH; $by++) {
|
|
$rgb = imagecolorat($image, $x + $bx, $y + $by);
|
|
$colors = imagecolorsforindex($image, $rgb);
|
|
$gray = $colors['red']; // Já está em escala de cinza
|
|
$sum += $gray;
|
|
$count++;
|
|
}
|
|
}
|
|
|
|
// Calcula threshold local
|
|
$threshold = ($count > 0) ? ($sum / $count) - $C : 128;
|
|
|
|
// Aplica threshold ao bloco
|
|
for ($bx = 0; $bx < $blockW; $bx++) {
|
|
for ($by = 0; $by < $blockH; $by++) {
|
|
$rgb = imagecolorat($image, $x + $bx, $y + $by);
|
|
$colors = imagecolorsforindex($image, $rgb);
|
|
$gray = $colors['red'];
|
|
|
|
if ($gray > $threshold) {
|
|
$color = imagecolorallocate($result, 255, 255, 255);
|
|
} else {
|
|
$color = imagecolorallocate($result, 0, 0, 0);
|
|
}
|
|
|
|
imagesetpixel($result, $x + $bx, $color);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
return $threshold;
|
|
}
|
|
|
|
private function analyzeImageQuality() {
|
|
$width = imagesx($this->srcImage);
|
|
$height = imagesy($this->srcImage);
|
|
|
|
// Inicializa métricas
|
|
$this->imageQualityMetrics = [
|
|
'brightness' => 0,
|
|
'contrast' => 0,
|
|
'noise' => 0,
|
|
'blur' => 0
|
|
];
|
|
|
|
// Calcula brilho médio e contraste
|
|
$histogram = array_fill(0, 256, 0);
|
|
$totalPixels = $width * $height;
|
|
|
|
for ($x = 0; $x < $width; $x++) {
|
|
for ($y = 0; $y < $height; $y++) {
|
|
$rgb = imagecolorat($this->srcImage, $x, $y);
|
|
$colors = imagecolorsforindex($this->srcImage, $rgb);
|
|
$gray = ($colors['red'] + $colors['green'] + $colors['blue']) / 3;
|
|
$histogram[(int)$gray]++;
|
|
}
|
|
}
|
|
|
|
// Calcula métricas de qualidade
|
|
$this->imageQualityMetrics['brightness'] = $this->calculateAverageBrightness($histogram, $totalPixels);
|
|
$this->imageQualityMetrics['contrast'] = $this->calculateImageContrast($histogram, $totalPixels);
|
|
$this->imageQualityMetrics['noise'] = $this->estimateImageNoise();
|
|
$this->imageQualityMetrics['blur'] = $this->estimateBlurLevel();
|
|
|
|
return $this;
|
|
}
|
|
|
|
private function determineOptimalParameters() {
|
|
// Define parâmetros baseados nas métricas
|
|
$this->processingParams = [
|
|
'contrast_adjustment' => $this->calculateContrastAdjustment(),
|
|
'brightness_adjustment' => $this->calculateBrightnessAdjustment(),
|
|
'sharpen_intensity' => $this->calculateSharpenIntensity(),
|
|
'noise_reduction' => $this->calculateNoiseReduction()
|
|
];
|
|
|
|
return $this;
|
|
}
|
|
|
|
private function applyAdaptiveEnhancements() {
|
|
// Aplica melhorias com parâmetros otimizados
|
|
if ($this->processingParams['brightness_adjustment'] != 0) {
|
|
imagefilter($this->processedImage, IMG_FILTER_BRIGHTNESS,
|
|
$this->processingParams['brightness_adjustment']);
|
|
}
|
|
|
|
if ($this->processingParams['contrast_adjustment'] != 0) {
|
|
imagefilter($this->processedImage, IMG_FILTER_CONTRAST,
|
|
$this->processingParams['contrast_adjustment']);
|
|
}
|
|
|
|
if ($this->processingParams['noise_reduction'] > 0) {
|
|
$this->applyAdaptiveNoiseReduction($this->processingParams['noise_reduction']);
|
|
}
|
|
|
|
if ($this->processingParams['sharpen_intensity'] > 0) {
|
|
$this->applyAdaptiveSharpening($this->processingParams['sharpen_intensity']);
|
|
}
|
|
|
|
return $this;
|
|
}
|
|
|
|
private function calculateAverageBrightness($histogram, $totalPixels) {
|
|
$sum = 0;
|
|
for ($i = 0; $i < 256; $i++) {
|
|
$sum += $i * $histogram[$i];
|
|
}
|
|
return $sum / $totalPixels;
|
|
}
|
|
|
|
private function calculateImageContrast($histogram, $totalPixels) {
|
|
$mean = $this->imageQualityMetrics['brightness'];
|
|
$variance = 0;
|
|
|
|
for ($i = 0; $i < 256; $i++) {
|
|
$variance += pow($i - $mean, 2) * ($histogram[$i] / $totalPixels);
|
|
}
|
|
|
|
return sqrt($variance);
|
|
}
|
|
|
|
private function estimateImageNoise() {
|
|
$width = imagesx($this->srcImage);
|
|
$height = imagesy($this->srcImage);
|
|
$noise = 0;
|
|
$samples = 1000;
|
|
|
|
// Amostragem aleatória para estimar ruído
|
|
for ($i = 0; $i < $samples; $i++) {
|
|
$x = rand(1, $width - 2);
|
|
$y = rand(1, $height - 2);
|
|
$noise += $this->calculateLocalVariance($x, $y);
|
|
}
|
|
|
|
return $noise / $samples;
|
|
}
|
|
|
|
private function estimateBlurLevel() {
|
|
// Implementa detecção de blur usando Laplacian
|
|
$laplacian = [
|
|
[0, 1, 0],
|
|
[1, -4, 1],
|
|
[0, 1, 0]
|
|
];
|
|
|
|
$temp = $this->applyConvolution($laplacian);
|
|
return $this->calculateVariance($temp);
|
|
}
|
|
|
|
private function calculateContrastAdjustment() {
|
|
$contrast = $this->imageQualityMetrics['contrast'];
|
|
$optimal = 128; // Valor ideal de contraste
|
|
|
|
if ($contrast < $optimal * 0.7) {
|
|
return min(50, (int)((1 - $contrast/$optimal) * 100));
|
|
} elseif ($contrast > $optimal * 1.3) {
|
|
return max(-50, (int)(($optimal/$contrast - 1) * 100));
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
private function calculateBrightnessAdjustment() {
|
|
$brightness = $this->imageQualityMetrics['brightness'];
|
|
$optimal = 128; // Valor ideal de brilho
|
|
|
|
return (int)(($optimal - $brightness) * 0.5);
|
|
}
|
|
|
|
private function calculateSharpenIntensity() {
|
|
$blurLevel = $this->imageQualityMetrics['blur'];
|
|
return min(100, (int)($blurLevel * 50));
|
|
}
|
|
|
|
private function calculateNoiseReduction() {
|
|
$noise = $this->imageQualityMetrics['noise'];
|
|
return min(5, (int)($noise * 2));
|
|
}
|
|
}
|